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Introduction

At IBM, we’re reimagining how networks think, reason, and heal themselves. Our Network Intelligence (INI) product brings AI-driven reasoning and automation to enterprise and telecom operations—combining time-series foundation models, agentic frameworks, and domain-aware knowledge graphs.

We’re looking for a Knowledge graph developer / engineer to help build the semantic core of this product.

You’ll balance the precision of a knowledge engineer with the pragmatism of a distributed-systems developer—turning ontologies into production-ready components that power real-world reasoning, correlation, and automation.

Your role and responsibilities

1. Build semantic foundations for AI reasoning

• Design and deliver domain-independent ontologies and knowledge graphs using a structured ontology pipelines (vocabulary → metadata → taxonomy → ontology → graph).
• Capture relationships, causal links, and operational context across network and service data.
• Ensure the resulting semantic models can support deterministic reasoning, explainable AI, and automated decision-making.

2. Engineer for scale and integration

• Develop and maintain APIs, pipelines, and microservices that operationalize ontologies and knowledge graphs in distributed AI environments.
• Integrate graph-driven reasoning into INI’s agentic and LLM-based frameworks.
• Apply sound software-engineering practices—versioning, testing, CI/CD—to semantic components.

3. Collaborate across disciplines

• Work with network subject-matter experts to capture key operational relationships.
• Partner with data scientists to align graph structures with event, metric, and time-series data.
• Co-design reasoning workflows with AI engineers integrating large language models, retrieval, and inference components.
• Operate in a fast-shipping product team where ontologies evolve alongside feature releases.

4. Govern, validate, and iterate

• Implement ontology governance, quality metrics, and performance validation.
• Measure semantic accuracy, reasoning precision, and end-to-end impact on automation outcomes.
• Balance conceptual rigor with product agility—building systems that work, not just models that theorize.
  
  

Your Impact on IBM Network Intelligence

You’ll define how AI understands networks—how it infers, reasons, and explains decisions in a transparent, traceable way. Your work will form the semantic trust layer for AI-driven network operations, helping enterprises and telecoms move from reactive monitoring to proactive, autonomous intelligence.

You’ll collaborate with some of IBM’s most talented AI researchers, data scientists, and network engineers, in an environment where ideas move rapidly from design to deployment.

Why Join Us

• Work on a pioneering AI product shaping the future of network autonomy.
• Collaborate with global experts across AI, networking, and semantic systems.
• Ship meaningful, high-impact features that reach live enterprise and telecom environments.
• Build your career in a team that values engineering excellence, scientific rigor, and creative experimentation.

Required education

Bachelor's Degree

Preferred education

Master's Degree

Required technical and professional expertise

• Strong experience in knowledge engineering (ontologies, taxonomies, semantic modeling).
• Proven software engineering background, preferably in distributed or data-intensive systems.
• Proficiency in at least one programming language (Python, Java, Go, etc.) and comfort with modern development toolchains.
• Experience building or consuming knowledge graphs or graph-based data systems.
• Strong systems-thinking and problem-solving ability—able to translate abstract relationships into production logic.
• Excellent communication and collaboration skills in multi-disciplinary environments.

Preferred technical and professional experience

• Familiarity with enterprise or telecom network operations, data models, or monitoring systems.
• Experience integrating semantic or graph systems with AI/ML pipelines or reasoning agents.
• Understanding of event-driven or time-series data representation.
• Curiosity to learn new technical domains and distill them into structured intelligence.
• Balance between rigor and pragmatism—able to build, test, and iterate quickly without losing semantic integrity.